I am looking for a solution to find the percentage area covered by a polygon inside another polygon, from geo coordinates using python.
The polygon can be either fully reside inside the other one or a portion of the second polygon.
Is there a solution to this.
Please advice.
Percentage is just area of intersection over area of the (other) polygon:
area(intersection)/area(polygon2).
Basically any of geometry packages should be able to compute this, as they all support area and intersection functions: I think Geopandas, SymPy, Shapely (and others I missed) should be able to do this. There might be differences in supported formats.
You did not specify what Geo coordinates you use though. I think Geopandas and SymPy support only 2D maps (flat map) - meaning you need to use appropriate projection to get exact result, and Shapely works with spherical Earth model.
Related
I have a dataset of georeferenced flickr posts (ca. 35k, picture below) and I have an unrelated dataset of georeferenced polygons (ca. 40k, picture below), both are currently panda dataframes. The polygons do not cover the entire area where flickr posts are possible. I am having trouble understanding how to sort many different points in many different polygons (or check if they are close). In the end I want a map with the points from the flickerdata in polygons colord to an attribute (Tag). I am trying to do this in Python. Do you have any ideas or recommendations?
Point dataframe Polygon dataframe
Since, you don't have any sample data to load and play with, my answer will be descriptive in nature, trying to explain some possible strategies to approach the problem you are trying to solve.
I assume that:
these polygons are probably some addresses and you essentially want to place the geolocated flickr posts to the nearest best-match among the polygons.
First of all, you need to identify or acquire information on the precision of those flickr geolocations. How off could they possibly be because of numerous sources of errors (the reason behind those errors is not your concern, but the amount of error is). This will give you an idea of a circle of confusion (2D) or more likely a sphere of confusion (3D). Why 3D? Well, you might have flickr post from a certain elevation on a high-rise apartment, and so, (x: latitude,y: longitude, z: altitude) all may be necessary to consider. But, you have to study the data and any other information available to you to determine the best option here (2D/3D space-of-confusion).
Once you have figured out the type of ND-space-of-confusion, you will need a distance metric (typically just a distance between two points) -- call this sigma. Just to be on the safe side, find all the addresses (geopolygons) within a radius of 1 sigma and additionally within 2 sigma -- these are your possible set of target addresses. For each of these addresses have a variable that calculates its distances of its centroid, and the four corners of its rectangular outer bounding box from the flickr geolocations.
You will then want to rank these addresses for each flickr geolocation, based on their distances for all the five points. You will need a way of identifying a flickr point that is far from a big building's center (distance from centroid could be way more than distance from the corners) but closer to it's edges vs. a different property with smaller area-footprint.
For each flickr point, thus you would have multiple predictions with different probabilities (convert the distance metric based scores into probabilities) using the distances, on which polygon they belong to.
Thus, if you choose any flickr location, you should be able to show top-k geopolygons that flickr location could belong to (with probabilities).
For visualizations, I would suggest you to use holoviews with datashader as that should be able to take care of curse of dimension in your data. Also, please take a look at leafmap (or, geemap).
References
holoviews: https://holoviews.org/
datshader: https://datashader.org/
leafmap: https://leafmap.org/
geemap: https://geemap.org/
I have a set of coordinates (Longitude and Latitude) in decimal notation, and I'm looking for a way to find the coordinates in a circle with variable radius around each location.
Here in above figure you shown clearly red location, first we want do draw a circle around the center point (red location) here we only know the latitude and longitude of that point so my question is, how to draw circle if we only know the latitude and longitude. (in python without using any module or library )
Second if draw the circle then how to get all the location that are inside the circle.
Please help me to achieve these two points in Python without any use of library or any other module.
You ask a question for drawing etc but you never mentioned on what you plotting on.
It sounds a pretty good usecase for matplotlib.basemap(have done a pretty big project embeded in PyQt5).Starting from what you using to visuallize ,plotting a circle is really easy.Also for your second question finding all points in a circle can be infinite depending on what your coordinates decimal number is , i think you just want to check after if a point belongs in this circle witch is fairly easy too.
I suggest you take a look to basemap or cartropy libraries.
Have fun.
I have a 3D object which is not hollow, so there are many 3D points. How would you determine which of these points of such an object (especially with a very curvaceous surface) are on the surface? I understand how to extract them, but I need either a function somewhat like libraryUNK.surfacePoint... Which I don't know.
Or better an understanding of what is considered to be a surface point, which I don't know either and couldn't (yet) develop (for myself) any proper definition.
I know I can do triangulation to get the surface. But I don't get what to do next, as I will be left now with a set of triangles, some of which are on the surface, some of which are not... but again I have no definition how to consider what's on surface and what is not...
It seems to me that you want to compute the convex hull of your 3D points cloud.
It's not an easy problem, but there's plently of solutions (and algorithms) to do that. One of the efficients one is called "convex hull". There's a ConvexHull function in scipy.spatial.
Here is the details with an example (2D, but it works in any dimension)
http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html
This function use the QHull library
http://www.qhull.org/
There's plently of ressources on the QHull page. There's also a Wikipedia page (Again, this is not the only method to compute convex hulls, you may want to try others):
https://en.wikipedia.org/wiki/Quickhull
Edit: after re-reading the question, it seems your volume may not be convex. Unfortunately, if it isn't, there's no way to tell whether a point is inside the volume or in the surface without additional informations on the volume or on the points cloud.
I am using python. Now I have some coordinates (earth plane coordinates) and I want to draw a convex polygon based on these coordinates. Besides, I need to save the polygon into a GeoJSON format and calculate the polygon area.
I heard that scipy.spatial can do this but I have no idea how to do that, besides, how to extract the polygon coordinates and calculate the area on earth?
Thanks
As far as I know, scipy.spatial does not include the functions you need.
GeoPandas would be suitable for this task. See for instance this example for calculating areas of a polynomial. It also allows to convert between different coordinates system and support output to GeoJSON format.
So I'm working on a piece of code to take positional data for a RC Plane Crop Duster and compute the total surface area transversed (without double counting any area). I cannot figure out how to calculate the area for a given period of operation.
Given the following Table Calculate the area the points cover.
x,y
1,2
1,5
4,3
6,6
3,4
3,1
Any Ideas? I've browsed Greens Theorem and I'm left without a practical concept in which to code.
Thanks for any advise
Build the convex hull from the given points
Algorithms are described here
See a very nice python demo + src
Calculate its area
Python code is here
Someone mathier than me may have to verify the information here. But it looks legit: http://www.wikihow.com/Calculate-the-Area-of-a-Polygon and fairly easy to apply in code.
I'm not entirely sure that you're looking for "Surface area" as much as you're looking for Distance. It seems like you want to calculate the distance between one point and the next for that list. If that's the case, simply use the Distance Formula.
If the plane drops a constant width of dust while flying between those points, then the area is simply the distance between those points times the width of the spray.
If your points are guaranteed to be on an integer grid - as they are in your example - (and you really are looking for enclosed area) would Pick's Theorem help?
You will have to divide the complex polygon approximately into standard polygons (triangles, rectangles etc) and then find area of all of them. This is just like regular integration (only difference is that you are yet to find a formula to approximate your data).
The above points are when you assume that you are forming a closed polygon with your data.
Use to QHull to triangulate the region, then sum the areas of the resulting triangles.
Python now conveniently has a library that implements the method Lior provided. https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.ConvexHull.html will calculate the convex hull for any N dimensional space and calculate the area/volume for you as well. See the example and return value attributes towards the bottom of the page for details.